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As computing systems are more frequently and more actively intervening in societally critical domains such as healthcare, education and governance, it is critical to correctly predict and understand the causal effects of these interventions. Without an A/B test, conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal reasoning. Much like machine learning libraries have done for prediction, "DoWhy" is a Python library that aims to spark causal thinking and analysis. DoWhy provides a unified interface for causal inference methods and automatically tests many assumptions, thus making inference accessible to non-experts.

causal-inference python3 graphical-modelspathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models. The package is specifically tailored to analyze sequential data which capture multiple observations of short, independent paths observed in an underlying graph topology. Examples for such data include user click streams in information networks, biological pathways, or traces of information propagating in social media. Unifying the analysis of pathways and temporal networks, pathpy also supports the extraction of time-respecting paths from time-stamped network data. It extends (and will eventually supersede) the package pyTempnets.

temporal-networks pathways analysis sequential-data data data-mining network-analysis networks graph model-selection machine-learning graphical-models multi-order temporal-correlationsCRFSuite 0.13 is a fork of Naoaki Okazaki's implementation of conditional random fields (CRFs). Please refer to the web site for more information about the original software. To invoke tree-structured CRFs, you should provide the option --type=tree when running crfsuite learn and also specify this option when you later envoke crfsuite tag with the trained model.

machine-learning crf graphical-modelsThe first few chapters also serve as the basis of a workshop, and include a brief introduction to R that will be enough for one to follow along with the tools used (e.g. psych, lavaan, and mediation packages). The actual document can be found at https://m-clark.github.io/sem.

sem structural-equation-modeling latent-variable-models graphical-models growth-curves mixture-model r lavaan irt topic-modeling bayesian-nonparametric-modelsQM (QP Modeler) is a freeware graphical modeling tool for designing and implementing real-time embedded software based on the UML state machines and the lightweight QP active object frameworks. QM is available for Windows 32/64-bit, Linux 64-bit, and MacOS. CAUTION: If you have any previous version of QM installed on your system, please uninstall it before installing the new version.

uml uml-state-machine hierarchical-state-machine modeling-tool state-machine embedded-systems code-generation samek qp free graphical-models fsm object-oriented statechart state-diagram code-generatorA Structural Equation Modeling package that encourages users to treat model specifications as something to be generated and manipulated programmatically. Example models which OpenMx can fit include confirmatory factor, multiple group, mixture distribution, categorical threshold, modern test theory, differential equations, state space, and many others.

statistics psychology r c-plus-plus structural-equation-modeling behavior-genetics openmx estimation graphical-models multilevel-models item-response-theory growth-curvesLoMRF is an open-source implementation of Markov Logic Networks (MLNs) written in Scala programming language. Latest documentation.

machine-learning logic probabilistic-programming inference graphical-modelsMarkov networks are undirected graphical models that are widely used to infer relations between genes from experimental data. Their state-of-the-art inference procedures assume the data arise from a Gaussian distribution. High-throughput omics data, such as that from next generation sequencing, often violates this assumption. Furthermore, when collected data arise from multiple related but otherwise nonidentical distributions, their underlying networks are likely to have common features. New principled statistical approaches are needed that can deal with different data distributions and jointly consider collections of data sets. FuseNet is a Markov network formulation that infers networks from a collection of nonidentically distributed data sets. FuseNet is computationally efficient and general: given any number of distributions from an exponential family, FuseNet represents model parameters through shared latent factors that define neighborhoods of network nodes. Network inference methods for non-Gaussian data, such as FuseNet, can help in accurate modeling of the data generated by emergent high-throughput technologies.

graphical-models exponential-family heterogeneous-network network-inference diverse-distributions fusing-dataThe Graphical Language Server Protocol Framework provides extensible components to enable the development of diagram editors including edit functionality in (distributed) web-applications via a client-server protocol. This Graphical Language Server Protocol (GLSP) is work in progress and developed in collaboration among TypeFox, Obeo, and EclipseSource. It follows the same architectural pattern as the Language Server Protocol for textual languages, but applies it to graphical modeling for browser/cloud-based deployments. The protocol as well as the client implementation is heavily based on Sprotty but extends it with editing functionality and GLSP-specific communication with the server. Below is a screenshot of a small example diagram being edited in the GLSP client, as well as the server log printing the GLSP actions processed on the server during the current editing session. Click on the image below to see it in action.

diagram modeling lsp-server lsp sprotty theia-ide graphical-models
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